Wavelet Fuzzy Neural Supertwisting Sliding Mode Control of an Active Power Filter

Abstract

This paper presents a new intelligent sliding mode control approach to effectively achieve harmonic suppression of an active power filter (APF). An intelligent complementary terminal sliding mode controller (CTSMC) is proposed to improve the control accuracy of current loop and deal with the lumped disturbances in the system. Complementary terminal sliding mode control combines the features of complementary sliding mode control (CSMC) and terminal sliding mode control (TSMC) and does not depend on an accurate dynamic model. In addition, in order to reduce chattering, a multi-loop neural network (MLNN) whose parameter learning laws of MLNN are derived based on the Lyapunov laws are proposed to approximate the unknown nonlinear function term in the APF dynamic model, thus reducing the burden of sliding mode control. Finally, within the computing power of control board, detailed simulation and hardware experiments are carried out to demonstrate it has better harmonic suppression, steady-state and dynamic performance compared with the existing methods.